Radiotherapy treatment involves subjecting a patient to a type of radiation, which is normally distributed over a number of beams delivered to the patient. A radiotherapy treatment plan contains a number of beam properties that specify the beam setup to be used when each beam of the plan is delivered to the patient. The beam properties depend on the treatment technique and include for example gantry angle, the leaf positions of the multi-leaf collimator (MLC), the jaw positions and the machine output in monitor units (MU). For intensity modulated treatment techniques, e.g. VMAT, SMLC and DMLC, as well as conformal arcs, each beam is divided into a number of control points or segments. For each control point or segment the machine output in MU (or the segment weight), the jaw positions and the MLC leaf positions are specified, together with other properties such as gantry and collimator angles. Other techniques involving the use of an MLC include 3DCRT and static arcs.
Treatment plans can be created in many different ways, well known to the person skilled in the art, including forward planning and inverse planning. In forward planning, the beam setups are set manually by for example drawing the MLC shape or based on geometrical projections. In inverse planning methods, the beam setups are the result of a treatment plan optimization that aims to fulfil specified requirements on the dose distribution. Inverse planning may be performed on the fluence map or on the machine parameters. In fluence map optimization, ideal fluence profiles are optimized to achieve the optimal dose distribution with respect to the objective function used. After the optimization the ideal fluence maps are converted into deliverable beam setups including jaw positions, MLC-positions and the MU of each control point (or segment). The conversion step usually introduces a deterioration of the dose distribution compared to the dose distribution of the ideal fluence profile. In direct machine parameter optimization the beam settings, such as the MLC positions and MU of each control point (or segment), are directly optimized and the resulting beam setups are deliverable. In this case there is no need for a conversion step. Therefore, direct machine parameter optimization has proven to improve treatment plan quality substantially for most treatment techniques. The present invention is mainly related to inverse planning, but can also be applied on any other planning technique such as forward planning.
In inverse planning it is difficult to know beforehand what the effect of modifying the optimization problem will be. If a plan is created and for example the maximum dose to an organ at risk is too high, it might be a good idea to try to decrease the maximum dose level or increase the weight of the objective function defined on that organ or structure and restart the optimization. It is not unusual that the dose according to the resulting plan will be reduced beyond what was intended and/or that the dose to some other structures like the tumor and some other organs at risk have become unacceptable due to conflicting objectives. Interpolation between the two plans enables real time navigation so that the best trade-off between the two plans can be achieved.
Common for the plan generation techniques is that a dose distribution is computed from the obtained beam settings and evaluated to determine the quality of the dose distribution. Independent of plan generation technique a fluence profile can be computed from the beam settings and stored as an intermediate step in the dose computation.
It is possible to create two or more alternative treatment plans with different priorities on the clinical goals using any of the planning techniques. In some cases, none of the plans generated in this way are quite satisfactory. In such situations it would be beneficial to interpolate between treatment plans. The goal of treatment plan interpolation is to interpolate the deliverable beam parameters of at least two treatment plans in such a way that the resulting dose distribution is approximately an interpolation of the dose distributions of the plans. This enables the exploration of conflicting objectives in what is experienced as real time. By controlling the interpolation weights it is possible to navigate in a combination of the generated plans to find the best trade-off between the conflicting goals.
The navigation can be performed in many different ways, e.g. using textboxes, sliders, or pushing the DVH curves, and the resulting dose distribution is constantly updated when the weights are updated. Systems are available in which the interpolation weight can be controlled using a text box or a slider and the result can be displayed in what is experienced as real time. Such systems are commercially available, for example the Multi Criteria Optimization function in applicant's RayStation systems.
For the interpolation to work properly it is important that the interpolation of the treatment beams of the different plans at least approximately generates a dose distribution that is an interpolation between the dose distributions of the different plans. Interpolation between radiotherapy treatment plans is fairly straightforward if the beams are represented as fluence profiles, because of the linear relationship between the fluence and the dose. An interpolation between the fluences of the plans followed by a dose calculation from the interpolated fluence gives the same dose distribution that would be the result from interpolating the dose distributions of the plans directly. However, interpolating fluence profiles requires a second step, where the resulting interpolated fluence profiles are converted to deliverable beam setups and the final dose is recalculated. This conversion step takes time and introduces deviations from the interpolated ideal dose distribution.
It would therefore be desirable to interpolate directly in the deliverable beam setups, since the actual result will then be visible immediately and the conversion step is not needed. However, attempts to interpolate between deliverable plans have proven difficult because the parameters of the deliverable beam setups are not linearly related to the fluence. Interpolating between the control points of two deliverable plans will in most situations not result in an interpolation between the two dose distributions.
US 2013/0304503 discloses an optimization method, which allows local optimization of a small part of a deliverable plan based on a small change in the dose distribution. In such cases, an interpolation between the initial and the optimized plan will often produce a result that can be used for deliverable plan interpolation. This method only works when the change to the dose in each optimization step is small enough, or the volume where the dose is changed is small enough.
It would be beneficial to be able to interpolate directly deliverable plans with significant differences between the dose distributions to enable navigation between substantial clinical goal trade-offs.